Identification method and system for automatically discovering geological disaster monitoring equipment based on laser radar

Abstract

The invention belongs to the technical field of geological disaster monitoring, and discloses an identification method and system for automatically discovering geological disaster monitoring equipment based on a laser radar. The method comprises the following steps: determining a relative position and geological disaster information connection variability; the error parameters are added to a geological disaster monitoring equipment movement track evolution model based on data driving and trained, geological disaster information movement changes and variances of adjacent connection areas are predicted, and geological disaster information connection variability of a prediction result is calculated; meanwhile, a predicted credibility index is obtained; track correction based on the laser radar is carried out; and constructing a laser radar digital-analog positioning recognition model based on Kalman filtering, obtaining a positioning recognition result of the automatic geological disaster discovery monitoring equipment, and obtaining geological disaster monitoring information of the position according to the positioning recognition result. Accurate geological disaster evolution information can still be obtained after the position of the geological disaster monitoring equipment is changed, and the method has practical significance for monitoring engineering.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological disaster monitoring, and in particular to an identification method and system for automatically discovering geological disaster monitoring equipment based on laser radar. Background Art

[0002] When a new geological disaster monitoring device is deployed at a corresponding geographical location, it is necessary to manually configure the driver corresponding to the new geological disaster monitoring device on the server so that the server can parse the geological disaster monitoring data sent by the new geological disaster monitoring device. However, this method requires manual configuration, is not very flexible, and has low efficiency.

[0003] To solve the above problems, the prior art invention patent (publication number CN114625382A, publication date 20220614) provides a method for automatically discovering geological disaster monitoring equipment. After the new geological disaster monitoring equipment is deployed at the corresponding location, the database adds the equipment information of the newly deployed geological disaster monitoring equipment in real time, and the server obtains the equipment information of the newly deployed geological disaster monitoring equipment from the database, and judges whether the driver corresponding to the newly deployed geological disaster monitoring equipment has been installed according to the equipment information; if it is judged that the driver corresponding to the newly deployed geological disaster monitoring equipment has not been installed, the driver corresponding to the newly deployed geological disaster monitoring equipment is matched and obtained from the driver pool and then installed, so as to obtain and parse the geological disaster monitoring data sent by the newly deployed geological disaster monitoring equipment. Automatic installation of the driver of the newly deployed geological disaster monitoring equipment is realized.

[0004] LiDAR systems use light pulses to measure the distance to an object based on the time of flight (TOF) of each light pulse. Light pulses emitted from the light source of the LiDAR system interact with far-side objects. A portion of the light reflects from the object and returns to the detector of the LiDAR system. Based on the time elapsed between the emission of the light pulse and the detection of the returning light pulse, the distance is estimated. In some examples, a laser transmitter generates the light pulses. The light pulses are focused by a lens or lens assembly. The time taken for the laser pulse to return to a detector mounted near the transmitter is measured. A highly accurate distance is derived from the time measurement.

[0005] 3D point clouds are needed in many operational scenarios. Many schemes have been used to interrogate the surrounding environment in three dimensions. In some examples, a 2D instrument is actuated up and down and / or back and forth, often on a gimbal. This is often referred to in the art as "blinking" or "nodding" the sensor. Thus, a single-beam lidar unit can be employed to capture an entire 3D array of range points, albeit one at a time. In a related example, a prism is employed to "divide" the laser pulse into multiple layers, each at a slightly different vertical angle. This simulates the nodding effect described above, but without actuating the sensor itself.

[0006] In many applications, a sequence of pulses is emitted. The direction of each pulse changes in quick succession. In these examples, the distance measurement associated with each individual pulse can be considered a pixel, and the collection of pixels emitted and captured in quick succession (i.e., a "point cloud") can be presented as an image or analyzed for other reasons (e.g., detecting obstacles). In some examples, viewing software is used to present the resulting point cloud as an image that appears three-dimensional to the user.

[0007] Through the above analysis, the problems and defects of the prior art are as follows: usually geological disaster monitoring equipment includes one or more of a rain gauge, a soil moisture meter, a water level gauge, a piezometer, a crack meter, a surface displacement monitor and an inclinometer. The base station for parsing the data information of the geological disaster monitoring equipment includes multiple relay devices. These data collection, driving and parsing are usually based on the fact that the monitoring position of the geological disaster monitoring equipment remains unchanged during a period of time to collect and process the corresponding information. However, in long-term monitoring tasks, the fixed position of the geological disaster monitoring equipment may change due to geological changes. At this time, all information may change, which may be mistaken for a change in the geological disaster, giving an erroneous signal to the subsequent preventive measures. Therefore, relying solely on the prior art, it is impossible to obtain accurate geological disaster evolution information after the position of the geological disaster monitoring equipment changes. For this reason, this application proposes a method for automatically discovering and identifying geological disaster monitoring equipment based on lidar, which has practical significance for monitoring projects. Summary of the invention

[0008] In order to overcome the problems existing in the related art, the disclosed embodiment of the present invention provides a method and system for automatically discovering geological disaster monitoring equipment based on laser radar.

[0009] The technical solution is as follows: a method for automatically discovering geological disaster monitoring equipment based on laser radar, comprising:

[0010] S1, construct and train a data-driven geological hazard monitoring equipment movement trajectory evolution model, and use the trained model and the data measured by the geological hazard monitoring equipment sensors to determine the relative position and geological hazard information connection variability;

[0011] S2, adding the error parameters to the data-driven geological hazard monitoring equipment movement trajectory evolution model for training, predicting the movement change and variance of geological hazard information in adjacent connection areas, calculating the connection variability of geological hazard information in the prediction results, and obtaining the prediction credibility index;

[0012] S3, performing trajectory correction based on laser radar;

[0013] S4, constructs a laser radar digital-analog positioning and recognition model based on Kalman filtering, tightly couples the laser radar spectrum ranging observation through the extended Kalman filtering, and obtains the automatic detection of geological disaster monitoring equipment positioning and recognition results based on the high-frequency displacement rate vector output by the data-driven geological disaster monitoring equipment movement trajectory evolution model, and obtains the geological disaster monitoring information at that location based on the positioning and recognition results.

[0014] In step S1, a data-driven geological disaster monitoring equipment movement trajectory evolution model is constructed and trained, including:

[0015] Construct the data-driven part, and use the data of the built-in acceleration monitor and gyroscope of the geological disaster monitoring equipment to estimate the moving speed v of the geological disaster monitoring equipment. The expression is:

[0016] v=[v x v y v z ] T

[0017] In the formula, v x is the lateral velocity, v y is the longitudinal velocity, v z is the speed of change in spatial direction, T is the order;

[0018] The model driving part is constructed, the velocity vector is combined with the ranging results based on the lidar wave, and the extended Kalman filter is used to estimate the position, displacement direction and velocity of the geological disaster monitoring equipment.

[0019] Furthermore, before constructing and training the data-driven geological disaster monitoring equipment movement trajectory evolution model, quaternion-based normalization preprocessing is required, including:

[0020] The attitude quaternion is calculated using a gradient descent algorithm, and the acceleration and angular velocity are converted from the sensor coordinate system to the navigation coordinate system using the attitude quaternion. The vector β in the sensor coordinate system IMU The following formula is used to convert β into the navigation coordinate system INS , the expression is:

[0021]

[0022] In the formula, α is conjugated, is the complex conjugate of the quaternion;

[0023] Quaternions are used to rotate vectors and convert them into Euler angles. The order of defining the attitude angles of sensors in geological disaster monitoring equipment is:<z,x,y> , where z is the azimuth angle, x is the pitch angle, and y is the roll angle.

[0024] In step S1, based on the data measured by the sensors of the geological disaster monitoring equipment, the relative position and the variability of the connection of geological disaster information are determined, including:

[0025] The sliding window method is adopted. Using a sliding window of fixed size, the sensor data of geological disaster monitoring equipment is divided into independent sequences; the window size n of the sequence is 100 frames, and the shortest distance of the sliding window is 5 frames; the velocity vector is predicted by the deep neural network of each sequence, and the position is generated by merging the link module, and the current position is updated to 100 frames; the prediction update is performed using overlapping windows, and the output frequency is increased to 40Hz. The position obtained by processing the velocity vector through a low-pass filter is used to smoothly reconstruct the predicted trajectory;

[0026] Public standard data sets and self-collected data sets are used as training sets and test sets. During the training phase, the features and labels of each window are randomly rotated to enhance the independence of the displacement direction of the data. The training goal is to minimize the mean square error between the estimated value and the true value provided by the data set. The optimal parameters are obtained through ADAM optimizer training. After training on the GPU, the model is directly applied to the local geological disaster monitoring equipment sensor to infer the displacement trajectory of the geological disaster monitoring equipment.

[0027] In step S2, the variability of the geological disaster information connection of the prediction result is calculated to obtain the prediction credibility index, including:

[0028] The total geological hazard information connection variability of network prediction is defined as σ total , the expression is:

[0029] σ total =var p(y|x) (y)

[0030] In the formula, var p(y|x) (y) is the predicted value of probability distribution in the connection change of total geological hazard information, and p(y|x) is the conversion function between roll angle y and pitch angle x;

[0031] According to the source of geological hazard information connection variability, it is divided into data geological hazard information connection variability σ total and the model geological hazard information connection variability σ model , the data geological disaster information connection variability indicates the anti-noise performance of the network for the input data;

[0032] The variability of geological hazard information connection is based on the Bayesian belief network. With the help of the expected propagation EP framework and the density filtering ADF method, the general network layer of ResNet18 is overwritten by mathematical reasoning. The network layer of geological hazard information connection variability is defined as follows:

[0033] For any network layer c (i) =h(i) (c (i-1) ,θ (i) ), the geological disaster information connection variable propagation layer is realized based on the following formula:

[0034]

[0035] In the formula, h (i) is the i-th propagation layer, c (i-1) is the i-1th network layer, θ (i) is the change value of the i-th network layer, To connect the geological disaster information with the variable transmission layer sampling prediction value, The variance in the sampling prediction of the variable propagation layer for the geological hazard information connection is is the sampling prediction method in the i-1th network layer, is the method of sampling prediction variance in the i-1th network layer;

[0036] For the variability of the connection of model geological disaster information, the expectation and variance are output through Monte Carlo sampling. The dropout layer after the fixed activation layer ReLU is in training mode, and the parameters will be randomly discarded; the noise variance is initialized and propagated together with the test data to obtain several groups of sampled prediction values ​​b and corresponding variance ζ; the credibility of the network for the output is evaluated to obtain the variability of the connection of model geological disaster information.

[0037] Furthermore, the credibility of the network output is evaluated to obtain the variability of the model geological disaster information connection, including: the network undergoes several propagations and Monte Carlo sampling to obtain several groups of prediction values ​​b i and variance ζ i , calculate the model uncertainty γ model , the expression is:

[0038] γ model =var(b i )

[0039] In the formula, var() is the variance calculation;

[0040] After Monte Carlo sampling, the variability of the data geological disaster information connection is expressed as the mean of several sampling prediction values, and then the total uncertainty of the network γ total , the expression is:

[0041] γ total =γ data +γ model

[0042] In the formula, γ data is the uncertainty of the current model.

[0043] In step S3, trajectory correction based on laser radar is performed, including:

[0044] When the LiDAR magnetic field in the environment is stable, there is only one stable LiDAR magnetic field vector, and the three-dimensional magnetic vector of the magnetometer changes with the attitude of the IMU; when the attitude change of the IMU does not match the change of the magnetic vector, the LiDAR magnetic field environment is unstable under the influence of strong magnets; the attitude change of the IMU is calculated using a gyroscope and expressed in the form of a quaternion. i ; Use quaternion to convert magnetic vector m i Convert to Calculate m i+1 and The modulus of the vector difference is taken as the matching degree su; the larger the matching degree, the greater the attitude difference between the IMU and the magnetic vector, and the unstable magnetic field of the lidar; m i+1 is the i+1th magnetic vector, is the i+1th magnetic vector after transformation;

[0045] The laser radar magnetic field strength reflects the stability of the laser radar environment. The sliding standard deviation std of the laser radar magnetic field strength is used as the condition for determining the stability of the laser radar magnetic field. The size of the sliding window is set to 100 frames, and the stability of the laser radar magnetic field Defined as:

[0046]

[0047] In the formula, Seek guidance for Renault, is the stability value of the lidar magnetic field in the window size d, is the sliding average, thrshd1 is the first window of sliding window size d, and thrshd2 is the second window of sliding window size d;

[0048] When the average sliding standard deviation and the average matching degree are less than the threshold, the lidar magnetic field is completely stable, and the stability of the lidar magnetic field Set to 1, the stability of the lidar magnetic field The range is from 0 to 1, indicating from unstable to stable;

[0049] Select the stable LiDAR magnetic field environment and low geological disaster information, and connect the correlation quantity on the variable straight sequence as the anchor point g k If the posture of the geological disaster monitoring equipment sensor is not changed, the correlation value of each straight sequence will fluctuate in a small range; when the fluctuation is greater than the threshold, it is determined that the relative horizontal posture relationship between the geological disaster monitoring equipment sensor and the person has changed, and it is necessary to update g k ; Using g k Calculate the correction angle for a straight sequence The expression is:

[0050]

[0051] In the formula, ft i is the average stability of the lidar magnetic field, g i is the i-th anchor point;

[0052] By using the shortest distance calculation method of the neural network, the correction angle of the straight sequence is predicted Realize adaptive correction of trajectory.

[0053] In step S4, a laser radar digital-analog positioning recognition model based on Kalman filtering is constructed, including:

[0054] S4.1, build the system model and observation model, and use the data-driven shortest distance calculation method to adopt the local coordinate system A system (x A ,y A ,α A ), select the Northeast Sky coordinate system as the absolute coordinate system B (e B ,n B ,α B ), where α is the displacement direction and the system state vector to be estimated is X p Described as:

[0055] X p =[e p n p θ p ] T

[0056] In the formula, (e p ,n p ) are the east and north coordinates of the target in the B system at time P, θ p It is the right-handed displacement angle of system A relative to system B, including the initial deflection angle of the two coordinate systems at the moment of positioning service startup and the accumulated error deflection angle of the sensor. Driven by the data of the shortest distance calculation method, the state transition of the system is as follows:

[0057]

[0058] Where h() is the state vector function to be estimated, O p is the initial state value at time p, e p-1 is the east direction coordinate at time p-1, n p-1 is the north direction coordinate at time p-1, θ p-1 is the right-handed displacement angle of system A relative to system B at time p-1, X p-1 is the system state vector to be estimated at time p-1, Δx p ,Δyp They are the displacement increments output in the horizontal and vertical directions of the shortest distance calculation method in the local coordinate system from time p-1 to time p;

[0059] The laser radar spectrum positioning estimation adopts the time division multiple access and frequency division multiple access strategies to form a laser radar spectrum signal network; the observation data including at least two laser radar spectra are solved, and the observation equation is obtained through geometric relations, which is expressed as follows:

[0060]

[0061] In the formula, C p is the observed data at time p, q() is the time division multiple access estimation function, E p is the frequency division multiple access estimated value, F1 is the first time division address, F2 is the second time division address, F i-1 is the i-1th time division address, F i is the i-th time division address, F m-1 is the time division address of the lidar spectrum estimated at the m-1th epoch, F m is the time division address of the lidar spectrum estimated in the mth epoch, i is the number, g is the speed of sound propagation at temperature T, and m is the number of lidar spectra estimated in this epoch;

[0062] The model is initialized and model parameters are dynamically adjusted.

[0063] Further, the model is initialized, including:

[0064] The observation information is combined with the simple particle filter of the laser radar to calculate the initial value of the plane coordinates of the geological disaster monitoring equipment sensor; the coordinate data contained in the observation information gives the regional range of the geological disaster monitoring equipment sensor, and a certain number of particles are evenly distributed within the range. Through the resampling operation, the particle attributes [e B n B ] T , then the particle set is initialized as:

[0065]

[0066] In the formula, e min ,e max ,n min ,n max is the region boundary defined by the initial observation information, N is the number of particles, e l is the property of the lth particle pointing eastward, n l is the property of the lth particle in the north, L() is the particle property function of the boundary constraint condition of the region defined by the initial observation information;

[0067] The overall error of the LiDAR spectrum estimation follows a normal distribution. According to the prediction residual δ, the weight of each particle is:

[0068]

[0069] σ=C0-C l

[0070] In the formula, w l is the weight of the lth particle, R l is the radius of particle distribution, exp{} is the residual prediction function, σ l is the prediction residual of the lth particle, C0,C l are the initial observation and the predicted observation respectively;

[0071] After normalizing the particle set weights and using the random resampling method to assign them to a new particle set, the initial value estimate is obtained by averaging:

[0072]

[0073] Where, e0 is the original particle property of the east-downward direction, n0 is the original particle property of the north-downward direction, and e l is the property of the lth particle pointing eastward, n l It is the property of the lth particle in the north and downward direction;

[0074] Dynamically adjust model parameters, including:

[0075] Given the initial system error covariance matrix W0 = I 3×3 Under the premise of , the displacement direction angle converges rapidly, the basic weight of the observation information is improved in the early stage of filtering, and the process noise is amplified, and the observation noise corresponding to each lidar observation data is adjusted. The expression is:

[0076]

[0077] In the formula, is the i-th diagonal element of the observation noise covariance matrix, is the initial diagonal element of the observation noise, is the prediction residual of the p+1th lidar spectrum, is the prediction residual of the pth lidar spectrum, P0 is the variance expansion threshold;

[0078] The geological disaster information connection variability factor of the data-driven shortest distance calculation method model can provide timely guidance on adjusting the status and observation weight when abnormal posture updates caused by sudden changes in the lidar are detected.

[0079] Another object of the present invention is to provide a laser radar-based automatic discovery geological disaster monitoring equipment recognition system, the system implements the laser radar-based automatic discovery geological disaster monitoring equipment recognition method, the system comprises:

[0080] The module for determining the variability of the connection between the position and geological hazard information is used to build and train a data-driven geological hazard monitoring equipment movement trajectory evolution model, and determine the relative position and the variability of the connection between the geological hazard information based on the trained model and the data measured by the geological hazard monitoring equipment sensor;

[0081] The credibility index prediction module is used to add error parameters to the data-driven geological hazard monitoring equipment movement trajectory evolution model for training, predict the movement changes and variances of geological hazard information in adjacent connection areas, calculate the variability of geological hazard information connection in the prediction results, and obtain the credibility index of the prediction;

[0082] A trajectory correction module, used for performing trajectory correction based on lidar;

[0083] The geological disaster monitoring information identification module is used to build a laser radar digital-analog positioning and identification model based on Kalman filtering. By extending the Kalman filter to tightly couple the laser radar spectrum ranging observation, and based on the high-frequency displacement rate vector output by the data-driven geological disaster monitoring equipment movement trajectory evolution model, the positioning and identification results of the automatic geological disaster monitoring equipment are obtained, and the geological disaster monitoring information at the location is obtained according to the positioning and identification results.

[0084] In combination with all the above-mentioned technical solutions, the beneficial effects of the present invention are as follows: the present invention can monitor the monitoring position information of geological disaster monitoring equipment in real time during a certain period of time, correctly identify the changed position, and seamlessly connect with the information of the original position, so as to further obtain accurate information on the evolution of geological disasters, and will not be mistaken for abnormal and large changes in geological disasters. It can give an accurate signal for subsequent preventive measures, and solve the problem of still being able to obtain accurate geological disaster evolution information after the position of the geological disaster monitoring equipment changes, which has practical significance for monitoring projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0086] Figure 1 It is a flow chart of an identification method for automatically discovering geological disaster monitoring equipment based on laser radar provided by an embodiment of the present invention;

[0087] Figure 2Schematic diagram of an identification system for automatically discovering geological disaster monitoring equipment based on laser radar provided in an embodiment of the present invention;

[0088] In the figure: 1. Module for determining the variability of the connection between location and geological disaster information; 2. Module for predicting credibility indicators; 3. Module for trajectory correction; 4. Module for identifying geological disaster monitoring information. DETAILED DESCRIPTION

[0089] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0090] Embodiment 1, as Figure 1 As shown, the identification method for automatically discovering geological disaster monitoring equipment based on laser radar provided in an embodiment of the present invention includes:

[0091] S1, construct and train a data-driven geological hazard monitoring equipment movement trajectory evolution model, and use the trained model and the data measured by the geological hazard monitoring equipment sensors to determine the relative position and geological hazard information connection variability;

[0092] S2, adding the error parameters to the data-driven geological hazard monitoring equipment movement trajectory evolution model for training, predicting the movement change and variance of geological hazard information in adjacent connection areas, calculating the connection variability of geological hazard information in the prediction results, and obtaining the prediction credibility index;

[0093] S3, performing trajectory correction based on laser radar;

[0094] S4, constructs a laser radar digital-analog positioning and recognition model based on Kalman filtering, tightly couples the laser radar spectrum ranging observation through the extended Kalman filtering, and obtains the automatic detection of geological disaster monitoring equipment positioning and recognition results based on the high-frequency displacement rate vector output by the data-driven geological disaster monitoring equipment movement trajectory evolution model, and obtains the geological disaster monitoring information at that location based on the positioning and recognition results.

[0095] Embodiment 2, the recognition method for automatically discovering geological disaster monitoring equipment based on laser radar provided in the embodiment of the present invention includes:

[0096] Step 1: Multi-source fusion positioning architecture based on digital-analog combination;

[0097] The location estimation of the geological hazard monitoring equipment is achieved by fusing the LiDAR spectrum ranging with the geological hazard monitoring equipment sensor data. The data processing architecture for the location estimation of the geological hazard monitoring equipment mainly consists of two parts;

[0098] (1) Construct the data-driven part, and use the data of the built-in acceleration monitor and gyroscope of the geological disaster monitoring equipment to estimate the moving speed v of the geological disaster monitoring equipment. The expression is:

[0099] v=[v x v y v z ] T

[0100] In the formula, v x is the lateral velocity, v y is the longitudinal velocity, v z is the speed of change in spatial direction, T is the order;

[0101] (2) Construct the model driving part, combine the velocity vector with the ranging results based on the lidar wave, and use the extended Kalman filter to estimate the position, displacement direction and speed of the geological disaster monitoring equipment.

[0102] Since the sensors of geological hazard monitoring equipment have the characteristics of low power consumption and high data update frequency, they are widely used in positioning. However, the measurement results of low-cost geological hazard monitoring equipment sensors (accelerometers, gyroscopes, magnetometers, etc.) are easily affected by drift errors. For example, when the acceleration is directly integrated twice to calculate the displacement, the error grows almost geometrically. Taking the measurement values ​​of the sensors of geological hazard monitoring equipment as input, the present invention adopts a deep learning-based method to estimate the motion trajectory of the geological hazard monitoring equipment. This is a data-driven shortest distance calculation method, which is completely different from the model-driven method using image detection.

[0103] Step 2: Constructing a data-driven geological disaster monitoring equipment movement trajectory evolution model;

[0104] The shortest distance calculation method relies on the sensors of the geological hazard monitoring equipment to calculate and track the position trajectory of the geological hazard monitoring equipment. The traditional shortest distance calculation method mainly uses the shortest distance model formula to estimate the shortest distance, the gyroscope and magnetometer to estimate the direction, and then calculates the position of each straight line segment. Since the shortest distance model needs to adjust the parameters according to the characteristics of the geological hazard monitoring equipment, and the direction estimation needs to limit the monitoring posture of the sensors of the geological hazard monitoring equipment, the traditional shortest distance calculation method cannot be used in general scenarios. The data-driven shortest distance calculation method overwrites the probabilistic neural network with the geological hazard information connection variability neural network based on mathematical inference, and uses the pre-processed massive geological hazard monitoring equipment sensor data and the real trajectory to train a neural network model that adapts to different postures and different geological hazard monitoring equipment. The model is driven by the sensor data of the geological hazard monitoring equipment and outputs the relative position and the connection variability of the geological hazard information. Since the shortest distance calculation method has cumulative errors in the displacement direction, it is automatically corrected at the appropriate time by using the laser radar magnetic field stability detection algorithm combined with the geological hazard information connection variability output by the neural network.

[0105] 2.1 Quaternion-based normalization preprocessing;

[0106] Before inputting the sensor data of the geological disaster monitoring equipment into the neural network, the data needs to be preprocessed to reduce the complexity of the data so that the network can better fit the displacement model of the geological disaster monitoring equipment. After solving the attitude quaternion using the gradient descent algorithm through the 9-axis sensor data, the attitude quaternion is used to convert the acceleration and angular velocity from the sensor coordinate system to the navigation coordinate system. The vector β in the sensor coordinate system IMU It can be converted to β in the navigation coordinate system by the following formula INS , the expression is:

[0107]

[0108] In the formula, α is conjugated, is the complex conjugate of the quaternion;

[0109] Quaternions are used to rotate vectors and convert them into Euler angles. The order of defining the attitude angles of sensors in geological disaster monitoring equipment is:<z,x,y> , where z is the azimuth angle, x is the pitch angle, and y is the roll angle.

[0110] Since the initial coordinate system of each motion trajectory is different, and most actual positioning application scenarios require absolute global coordinates. Therefore, it is necessary to find the rotation relationship between each sequence navigation coordinate system and the global coordinate system. Through quaternions and attitude angles, the present invention can calculate the rotation relationship from the navigation coordinate system to the global coordinate system.

[0111] Quaternions can only be used to rotate vectors, but cannot calculate the angle of their own rotation. They need to be converted into Euler angles. The order of defining the attitude angles of sensors in geological disaster monitoring equipment is:<z,x,y> , namely the displacement direction angle, pitch angle and roll angle. Therefore, convert the quaternion into Euler angle in this order. The Z axis of the navigation coordinate system and the global coordinate system are consistent, and there is only a difference in the Y axis on the horizontal plane. Therefore, the displacement direction angle difference between the Euler angle and the attitude angle is used to convert the motion trajectory from the navigation coordinate system to the global coordinate system.

[0112] 2.2 Estimation of displacement trajectory of geological disaster monitoring equipment based on deep neural network;

[0113] The traditional shortest distance calculation algorithm obtains the displacement by quadratic integration of gyroscope and acceleration monitor data, which has a serious error accumulation problem. The present invention adopts a lightweight deep neural network to solve the infinite error accumulation problem of traditional inertial navigation, and uses the ResNet18 residual neural network framework to learn the inertial tracking of sensor data of geological disaster monitoring equipment.

[0114] The physical model of the traditional inertial navigation system is based on the Newtonian mechanism. The low-cost sensors of geological disaster monitoring equipment have high noise, and the displacement trajectory derivation has extremely strong continuity, so the error will accumulate rapidly in the displacement trajectory derivation process. Models based on estimating the shortest distance and the shortest distance direction (such as the shortest distance calculation method) contain implicit motion models, which will fail due to the evolution law of geological objects or changes in the use environment. In short, inertial navigation based on zero velocity update (ZeroVelocity Update, ZUPT) and navigation based on the shortest distance calculation method of steps are both limited by the assumptions of motion dynamics and sensor connection, while deep learning methods can extract high-level feature representations (velocity vectors) based on the periodic nature of the position change of geological disaster monitoring equipment and do not rely on geometric theory. At the same time, the deep learning framework based on the ResNet18 architecture reduces the computational complexity to a certain extent, providing the possibility for deployment to sensors of geological disaster monitoring equipment.

[0115] The present invention adopts a sliding window method to improve the output rate of neural network prediction. By using a fixed-size sliding window, the sensor data of the geological disaster monitoring equipment will be divided into independent sequences. The window size n of the sequence is 100 frames (1 second), and the shortest distance of the sliding window is 5 frames. The velocity vector is predicted by the deep neural network of each sequence, and the position is generated by merging the link module, and the current position is updated to the previous 100 frames. Using overlapping windows for prediction updates, the output frequency will be increased to 40Hz, and the position obtained by the velocity vector is further processed by a low-pass filter to smoothly reconstruct the predicted trajectory.

[0116] The present invention uses public standard data sets and self-collected data sets as training sets and test sets. During the training phase, the features and labels of each window are randomly rotated to enhance the independence of the displacement direction of the data. The training goal is to minimize the mean square error between the estimated value and the true value provided by the data set. The ADAM optimizer is used to train to obtain the optimal parameters. The learning rate can be set to 1e -5 , the batch size is set to 256. After training on the GPU, the model can be directly applied to the local geological disaster monitoring equipment sensor to infer the displacement trajectory of the geological disaster monitoring equipment.

[0117] 2.3 Geohazard information connection and variability network based on EP framework;

[0118] Deep neural network navigation is generally evaluated by the mean square error between the estimated value and the true value. However, the true value trajectory cannot be obtained during actual deployment, so the accuracy of the network prediction cannot be evaluated (even if the prediction deviation is large). Therefore, the present invention studies the variability of geological hazard information connection of deep learning habitual navigation, which represents the credibility of the output of the deep neural network model. The estimation of geological hazard information connection variability can quantify the degree of trust model prediction. By adding error parameters to the neural network framework and training, the movement changes and variances of geological hazard information in adjacent connection areas can be predicted, thereby inferring the variability of geological hazard information connection of the prediction results, and then providing credibility indicators for different prediction segments for subsequent fusion algorithms. The residual network displacement trajectory calculation framework is extended to the Bayesian model, and the probability network is overwritten as a geological hazard information connection variability network based on mathematical inference, so as to quickly estimate the variability of geological hazard information connection of inertial navigation in an unsupervised manner.

[0119] The total geological hazard information connection variability of network prediction is defined as σ total , the expression is:

[0120] σ total =var p(y|x) (y)

[0121] In the formula, var p(y|x) (y) is the predicted value of probability distribution in the connection change of total geological hazard information, and p(y|x) is the conversion function between roll angle y and pitch angle x;

[0122] According to the source of geological hazard information connection variability, it is divided into data geological hazard information connection variability σ data and the model geological hazard information connection variability σ model The variability of data geological disaster information connection indicates the network's anti-noise performance for input data, and the model uncertainty indicates the network's confidence level in the prediction.

[0123] The geological hazard information connection variability scheme is based on the Bayesian belief network. With the help of the expectation propagation (EP) framework, the assumed density filtering (ADF) method is used to overwrite the general network layer of ResNet18 through mathematical reasoning. The geological hazard information connection variability network layer is defined as follows:

[0124] Definition 1: For any network layer c (i) =h (i) (c (i-1) ,θ (i) ), the geological disaster information connection and changeability propagation layer is approximately realized based on the following formula:

[0125]

[0126] In the formula, h (i) is the i-th propagation layer, c (i-1) is the i-1th network layer, θ (i) is the change value of the i-th network layer, To connect the geological disaster information with the variable transmission layer sampling prediction value, The variance in the sampling prediction of the variable propagation layer for the geological hazard information connection is is the sampling prediction method in the i-1th network layer, is the method of sampling prediction variance in the i-1th network layer;

[0127] The geological hazard information connection variability network enables the noise variance initialized to the order of 1e-3 to propagate forward in the network, probabilizing the network layer of the deep model, and finally the network will obtain the mean and variance. For a single propagation, the mean μ represents the network prediction value, and the variance v represents the final output of the initial noise forward propagation, which is the data geological hazard information connection variability.

[0128] For the variability of model geological disaster information connection, the network outputs the expectation and variance through Monte Carlo sampling. The dropout layer after the fixed activation layer ReLU is in training mode, and the parameters are randomly discarded. The noise variance is initialized and propagated together with the test data to obtain several groups of sampled prediction values ​​b and corresponding variance ζ. The credibility of the network output (i.e., the variability of model geological disaster information connection) is evaluated through Definition 2.

[0129] Definition 2: After several propagations and Monte Carlo samplings as in Definition 1, the network obtains several sets of prediction values ​​b i and variance ζ i , calculate the model uncertainty γ model , the expression is:

[0130] γ model =var(b i )

[0131] In the formula, var() is the variance calculation;

[0132] After Monte Carlo sampling, the variability of the data geological disaster information connection is expressed as the mean of several sampling prediction values, and then the total uncertainty of the network γ total , the expression is:

[0133] γ total =γ data +γ model

[0134] γ data =mean(ζ i )

[0135] In the formula, γ data is the uncertainty of the current model, and mean() is the mean calculation.

[0136] Intuitively, the data geological hazard information connection variability is the result of noise propagation in the network, and it interacts with the input data in the network layer. Model uncertainty can be regarded as a test of the stability of network prediction when parameters are discarded. The geological hazard information connection variability network framework is used in the calculation of the total network geological hazard information connection variability, which avoids the computational complexity of the Bayesian network and can give the credibility index of the network prediction without affecting the network structure and output and without supervision, thereby providing a reference for the subsequent fusion algorithm.

[0137] 2.4 Trajectory correction based on lidar;

[0138] Since the trajectory predicted by the neural network has cumulative errors in the displacement direction, and this error cannot be eliminated by the model, it is necessary to combine the lidar information to correct the trajectory. The displacement direction angle in the lidar magnetic field information has a certain correlation with the body direction when traveling straight. The displacement direction angle is independent of time and is not affected by cumulative drift. It is an absolute quantity that can be used to correct the trajectory of the shortest distance calculation method. In order to obtain stable displacement direction angle information, it is necessary to design a lidar magnetic field stability detection algorithm to screen out stable and available displacement direction angles. The connection variability of the geological disaster information output by the neural network is used to screen out the available body directions of geological disaster monitoring equipment.

[0139] When the LiDAR magnetic field in the environment is stable, there is only one stable LiDAR magnetic field vector, and the three-dimensional magnetic vector of the magnetometer changes with the attitude of the IMU; when the attitude change of the IMU does not match the change of the magnetic vector, the LiDAR magnetic field environment is unstable under the influence of strong magnets; the attitude change of the IMU is calculated using a gyroscope and expressed in the form of a quaternion. i ; Use quaternion to convert magnetic vector m i Convert to Calculate m i+1 and The modulus of the vector difference is taken as the matching degree su; the larger the matching degree, the greater the attitude difference between the IMU and the magnetic vector, and the unstable magnetic field of the lidar; m i+1 is the i+1th magnetic vector, is the i+1th magnetic vector after transformation;

[0140] At the same time, the LiDAR magnetic field strength can also reflect the stability of the LiDAR environment. The sliding standard deviation std of the LiDAR magnetic field strength is used as one of the conditions for determining the stability of the LiDAR magnetic field. The size of the sliding window is set to 100 frames (0.5s). Finally, the stability of the LiDAR magnetic field is defined as the following formula:

[0141]

[0142] In the formula, Seek guidance for Renault, is the stability value of the lidar magnetic field in the window size d, is the sliding average, thrshd1 is the first window of sliding window size d, and thrshd2 is the second window of sliding window size d;

[0143] When the average sliding standard deviation and the average matching degree are less than the threshold, the lidar magnetic field is completely stable, and the stability of the lidar magnetic field Set to 1, the stability of the lidar magnetic field The range is from 0 to 1, indicating from unstable to stable;

[0144] Through the straight sequence, the present invention can obtain the horizontal rotation relationship between the body direction of the geological disaster monitoring equipment and the global coordinate system. The displacement direction angle represents the horizontal rotation relationship between the IMU coordinate system and the global coordinate system. Since the human body is in a periodic motion state during displacement, the straight displacement direction angle almost fluctuates around a value. The average value of the straight displacement direction angle is directly taken, and it is found that it can well reflect the horizontal orientation of the IMU when straight. The difference between the average displacement direction angle of each straight sequence and the average neural network shortest distance calculation method direction is used to represent the correlation c between the laser radar magnetic field information and the body direction i .

[0145] The stable lidar magnetic field environment and the correlation quantity on the straight sequence with low geological disaster information connection variability are selected as the anchor point g. k If the posture of the geological disaster monitoring equipment sensor is not changed, the correlation value of each straight sequence should only fluctuate in a small range. When the fluctuation is greater than a certain threshold, it can be determined that the relative horizontal posture relationship between the geological disaster monitoring equipment sensor and the person has changed significantly, and it is necessary to update g k Then use g k To calculate the correction angle of the straight sequence

[0146]

[0147] In the formula, ft i is the average stability of the lidar magnetic field, g i is the i-th anchor point;

[0148] By using the shortest distance calculation method of the neural network, the correction angle of the straight sequence is predicted Realize adaptive correction of trajectory.

[0149] Step 3: constructing a laser radar digital-analog positioning and recognition model based on Kalman filtering;

[0150] Compared with the single data source positioning method with many limitations, tightly coupling and complementing data of different dimensions and attributes can usually achieve better positioning accuracy and stability. The data-driven shortest distance calculation method algorithm has given fine-grained relative dynamic information in a short time, so it only needs to be supplemented by global observation information to build a model with the help of mathematical expressions to provide location services in an absolute coordinate system. Since lidar spectrum positioning estimation is a high-precision, high-privacy, and high-security positioning technology, and it is compatible with sensors of popular intelligent geological disaster monitoring equipment and supports an unlimited number of concurrent users, the present invention takes the shortest distance calculation method and lidar digital-analog positioning data as an example to give a general-purpose fusion positioning model based on the Kalman filter framework.

[0151] Construct the system model and observation model, and use the data-driven shortest distance calculation method to adopt the local coordinate system A (x A ,y A ,α A ), select the Northeast Sky coordinate system as the absolute coordinate system B (e B ,n B ,α B ), where α is the displacement direction and the system state vector to be estimated is X p Described as:

[0152] X p =[ep n p θ p ] T

[0153] In the formula, (e p ,n p ) are the east and north coordinates of the target in the B system at time P, θ p It is the right-handed displacement angle of system A relative to system B, including the initial deflection angle of the two coordinate systems at the moment of positioning service startup and the accumulated error deflection angle of the sensor. Driven by the data of the shortest distance calculation method, the state transition of the system is as follows:

[0154]

[0155] Where h() is the state vector function to be estimated, O p is the initial state value at time p, e p-1 is the east direction coordinate at time p-1, n p-1 is the north direction coordinate at time p-1, θ p-1 is the right-handed displacement angle of system A relative to system B at time p-1, X p-1 is the system state vector to be estimated at time p-1, Δx p ,Δy p They are the displacement increments output in the horizontal and vertical directions of the shortest distance calculation method in the local coordinate system from time p-1 to time p;

[0156] The laser radar spectrum positioning estimation adopts the time division multiple access and frequency division multiple access strategies to form a laser radar spectrum signal network; the observation data including at least two laser radar spectra are solved, and the observation equation is obtained through geometric relations, which is expressed as follows:

[0157]

[0158] In the formula, C p is the observed data at time p, q() is the time division multiple access estimation function, E p is the frequency division multiple access estimated value, F1 is the first time division address, F2 is the second time division address, F i-1 is the i-1th time division address, F i is the i-th time division address, F m-1 is the time division address of the lidar spectrum estimated at the m-1th epoch, F m is the time division address of the lidar spectrum estimated in the mth epoch, i is the number, g is the speed of sound propagation at temperature T, and m is the number of lidar spectra estimated in this epoch;

[0159] The model is initialized and model parameters are dynamically adjusted.

[0160] Model initialization, including:

[0161] The observation information is combined with the simple particle filter of the laser radar to calculate the initial value of the plane coordinates of the geological disaster monitoring equipment sensor; the coordinate data contained in the observation information gives the regional range of the geological disaster monitoring equipment sensor, and a certain number of particles are evenly distributed within the range. Through the resampling operation, the particle attributes [e B n B ] T , then the particle set is initialized as:

[0162]

[0163] In the formula, e min ,e max ,n min ,n max is the region boundary defined by the initial observation information, N is the number of particles, e l is the property of the lth particle pointing eastward, n l is the property of the lth particle in the north, L() is the particle property function of the boundary constraint condition of the region defined by the initial observation information;

[0164] The overall error of the LiDAR spectrum estimation follows a normal distribution. According to the prediction residual δ, the weight of each particle is:

[0165]

[0166] σ=C0-C l

[0167] In the formula, w l is the weight of the lth particle, R l is the radius of particle distribution, exp{} is the residual prediction function, σ l is the prediction residual of the lth particle, C0,C l are the initial observation and the predicted observation respectively;

[0168] After normalizing the particle set weights and using the random resampling method to assign them to a new particle set, the initial value estimate is obtained by averaging:

[0169]

[0170] In the formula, w l is the weight of the lth particle, R l is the radius of particle distribution, exp{} is the residual prediction function, σ l is the prediction residual of the lth particle, C0,C lare the initial observation and the predicted observation respectively; where e0 is the original particle property downward from the east, n0 is the original particle property downward from the north, and e l is the property of the lth particle pointing eastward, n l It is the property of the lth particle in the north and downward direction;

[0171] Dynamically adjust model parameters, including:

[0172] Given the initial system error covariance matrix W0 = I 3×3 Under the premise of , the displacement direction angle converges rapidly, the basic weight of the observation information is improved in the early stage of filtering, and the process noise is amplified, and the observation noise corresponding to each lidar observation data is adjusted. The expression is:

[0173]

[0174] In the formula, is the i-th diagonal element of the observation noise covariance matrix, is the initial diagonal element of the observation noise, is the prediction residual of the p+1th lidar spectrum, is the prediction residual of the pth lidar spectrum, P0 is the variance expansion threshold;

[0175] The geological disaster information connection variability factor of the data-driven shortest distance calculation method model can provide timely guidance on adjusting the status and observation weight when abnormal posture updates caused by sudden changes in the lidar are detected.

[0176] It can be seen from the above embodiments that the present invention, in terms of data drive, constructs a neural network of the shortest distance calculation method based on deep learning, trains the characteristics of the accelerometer and gyroscope observations, learns the velocity vector of the position change of the geological disaster monitoring equipment, and accurately calculates the displacement trajectory of the geological disaster monitoring equipment. The traditional shortest distance calculation method scheme is to use each meter as a unit to count the shortest distance. The shortest distance calculation method based on data drive can learn the whole process of movement, output with a 40Hz velocity vector, realize the high-frequency shortest distance calculation method position update, and give full play to the advantages of the high update rate of the sensor data of the geological disaster monitoring equipment. In the model part, by extending the Kalman filter, the high-precision laser radar spectrum ranging observation and the high-frequency displacement rate vector output by the shortest distance calculation method network are tightly coupled, and the positioning accuracy of 1cm is achieved at a position update rate of 40Hz. Compared with the traditional pure model-driven fusion positioning method, the present invention has obvious advantages in positioning update rate, positioning accuracy and positioning stability.

[0177] The present invention solves the double integration problem based on the data-driven shortest distance calculation method, avoids the problem of rapid error accumulation caused by cheap IMU, and releases various constraints during displacement.

[0178] The technical solution of the present invention solves the problem of achieving high-precision and high-update rate location services in underground space, so that underground positioning can be further applied in practice.

[0179] The technical solution of the present invention analyzes the gait characteristics of multimodal inertial sensor data, constructs a motion prediction network based on hybrid supervised representation learning and an assumed density filtering algorithm based on variational inference, trains a self-supervised high-precision displacement trajectory estimation model with the ability to evaluate the variability of the connection between output geological disaster information, fuses the model output with the heuristic recalibration of lidar information, effectively suppresses the accumulation and propagation of inertial navigation system errors, and breaks through the precise estimation of the displacement trajectory of data-driven geological disaster monitoring equipment in complex environments; proposes a fusion positioning method based on extended Kalman filtering, organically couples model-driven precise ranging and data-driven precise displacement trajectory, and establishes a new paradigm of coupled positioning driven by both data and models.

[0180] Embodiment 3, as Figure 2 As shown, the laser radar-based automatic discovery and identification system for geological disaster monitoring equipment provided by the embodiment of the present invention includes:

[0181] The module 1 for determining the variability of the connection between the position and geological hazard information is used to construct and train a data-driven geological hazard monitoring equipment movement trajectory evolution model, and determine the relative position and the variability of the connection between the geological hazard information based on the trained model and the data measured by the geological hazard monitoring equipment sensor;

[0182] Credibility index prediction module 2 is used to add error parameters to the data-driven geological disaster monitoring equipment movement trajectory evolution model for training, predict the movement change of geological disaster information in adjacent connection areas and its variance, calculate the connection variability of geological disaster information of the prediction results, and obtain the prediction credibility index;

[0183] A trajectory correction module 3, used for performing trajectory correction based on laser radar;

[0184] The geological disaster monitoring information identification module 4 is used to construct a laser radar digital-analog positioning and identification model based on Kalman filtering. By extending the Kalman filtering to tightly couple the laser radar spectrum ranging observation quantity, and based on the high-frequency displacement rate vector output by the data-driven geological disaster monitoring equipment movement trajectory evolution model, the positioning and identification results of the automatic geological disaster monitoring equipment are obtained, and the geological disaster monitoring information of the location is obtained according to the positioning and identification results.

[0185] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0186] Since the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present invention, their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0187] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment.

[0188] An embodiment of the present invention further provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps in any of the above-mentioned method embodiments when executing the computer program.

[0189] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0190] An embodiment of the present invention also provides an information data processing terminal, which is used to provide a user input interface to implement the steps in the above-mentioned method embodiments when executed on an electronic device. The information data processing terminal is not limited to geological disaster monitoring equipment sensors, computers, and switches.

[0191] An embodiment of the present invention further provides a server, which is used to provide a user input interface to implement the steps in the above method embodiments when executed on an electronic device.

[0192] An embodiment of the present invention provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0193] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0194] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A laser radar-based automatic identification method for geological disaster monitoring equipment, characterized in that: The method includes: S1, construct and train a data-driven geological hazard monitoring equipment movement trajectory evolution model, and use the trained model and the data measured by the geological hazard monitoring equipment sensors to determine the relative position and geological hazard information connection variability; S2, adding the error parameters to the data-driven geological hazard monitoring equipment movement trajectory evolution model for training, predicting the movement change and variance of geological hazard information in adjacent connection areas, calculating the connection variability of geological hazard information in the prediction results, and obtaining the prediction credibility index; S3, performing trajectory correction based on lidar; S4, constructs a laser radar digital-analog positioning and recognition model based on Kalman filtering, tightly couples the laser radar spectrum ranging observation through the extended Kalman filtering, and obtains the automatic detection of geological disaster monitoring equipment positioning and recognition results based on the high-frequency displacement rate vector output by the data-driven geological disaster monitoring equipment movement trajectory evolution model, and obtains the geological disaster monitoring information at that location based on the positioning and recognition results.

2. The method for automatically discovering geological disaster monitoring equipment based on laser radar according to claim 1 is characterized in that: In step S1, a data-driven geological disaster monitoring equipment movement trajectory evolution model is constructed and trained, including: Construct the data-driven part, and use the data of the built-in acceleration monitor and gyroscope of the geological disaster monitoring equipment to estimate the moving speed v of the geological disaster monitoring equipment. The expression is: v=[v x v y v z ] T In the formula, v x is the lateral velocity, v y is the longitudinal velocity, v z is the speed of change in spatial direction, T is the order; The model driving part is constructed, the velocity vector is combined with the ranging results based on the lidar wave, and the extended Kalman filter is used to estimate the position, displacement direction and velocity of the geological disaster monitoring equipment.

3. The method for automatically discovering geological disaster monitoring equipment based on laser radar according to claim 2 is characterized in that: Before building and training the data-driven geological disaster monitoring equipment movement trajectory evolution model, the quaternion-based normalization preprocessing is performed, including: The attitude quaternion is calculated using a gradient descent algorithm, and the acceleration and angular velocity are converted from the sensor coordinate system to the navigation coordinate system using the attitude quaternion. The vector β in the sensor coordinate system IMU The following formula is used to convert β into the navigation coordinate system INS , the expression is: In the formula, α is conjugated, is the complex conjugate of the quaternion; Quaternions are used to rotate vectors and convert them into Euler angles. The order of defining the attitude angles of sensors in geological disaster monitoring equipment is:<z,x,y> , where z is the azimuth angle, x is the pitch angle, and y is the roll angle.

4. The method for automatically discovering geological disaster monitoring equipment based on laser radar according to claim 3 is characterized in that: In step S1, based on the data measured by the sensors of the geological disaster monitoring equipment, the relative position and the variability of the connection of geological disaster information are determined, including: The sliding window method is adopted. Using a sliding window of fixed size, the sensor data of geological disaster monitoring equipment is divided into independent sequences; the window size n of the sequence is 100 frames, and the shortest distance of the sliding window is 5 frames; the velocity vector is predicted by the deep neural network of each sequence, and the position is generated by merging the link module, and the current position is updated to 100 frames; the prediction update is performed using overlapping windows, and the output frequency is increased to 40Hz. The position obtained by processing the velocity vector through a low-pass filter is used to smoothly reconstruct the predicted trajectory; Public standard data sets and self-collected data sets are used as training sets and test sets. During the training phase, the features and labels of each window are randomly rotated to enhance the independence of the displacement direction of the data. The training goal is to minimize the mean square error between the estimated value and the true value provided by the data set. The optimal parameters are obtained through ADAM optimizer training. After training on the GPU, the model is directly applied to the local geological disaster monitoring equipment sensor to infer the displacement trajectory of the geological disaster monitoring equipment.

5. The method for automatically discovering geological disaster monitoring equipment based on laser radar according to claim 1 is characterized in that: In step S2, the variability of the geological disaster information connection of the prediction result is calculated to obtain the prediction credibility index, including: The total geological hazard information connection variability of network prediction is defined as σ total , the expression is: σ total =var p(y|x) (and) In the formula, var p(y|x) (y) is the predicted value of probability distribution in the connection change of total geological hazard information, and p(y|x) is the conversion function between roll angle y and pitch angle x; According to the source of geological hazard information connection variability, it is divided into data geological hazard information connection variability σ total and the model geological hazard information connection variability σ model , the data geological disaster information connection variability indicates the anti-noise performance of the network for the input data; The variability of geological hazard information connection is based on the Bayesian belief network. With the help of the expected propagation EP framework and the density filtering ADF method, the general network layer of ResNet18 is overwritten by mathematical reasoning. The network layer of geological hazard information connection variability is defined as follows: For any network layer c (i) =h (i) (c (i-1) ,θ (i) ), the geological disaster information connection variable propagation layer is realized based on the following formula: In the formula, h (i) is the i-th propagation layer, c (i-1) is the i-1th network layer, θ (i) is the change value of the i-th network layer, To connect the geological disaster information with the variable transmission layer sampling prediction value, The variance in the sampling prediction of the variable propagation layer for the geological hazard information connection is is the sampling prediction method in the i-1th network layer, is the method of sampling prediction variance in the i-1th network layer; For the variability of the connection of model geological disaster information, the expectation and variance are output through Monte Carlo sampling. The dropout layer after the fixed activation layer ReLU is in training mode, and the parameters will be randomly discarded; the noise variance is initialized and propagated together with the test data to obtain several groups of sampled prediction values ​​b and corresponding variance ζ; the credibility of the network for the output is evaluated to obtain the variability of the connection of model geological disaster information.

6. The method for automatically discovering geological disaster monitoring equipment based on laser radar according to claim 5 is characterized in that: Evaluate the credibility of the network output and obtain the variability of the model geological disaster information connection, including: the network undergoes several propagations and Monte Carlo sampling to obtain several groups of prediction values ​​b i and variance ζ i , calculate the model uncertainty γ model , the expression is: y model =var(b i ) In the formula, var( ) is the variance calculation; After Monte Carlo sampling, the variability of the data geological disaster information connection is expressed as the mean of several sampling prediction values, and then the total uncertainty of the network γ total , the expression is: c total =c data +g model In the formula, γ data is the uncertainty of the current model.

7. The method for automatically discovering geological disaster monitoring equipment based on laser radar according to claim 1 is characterized in that: In step S3, trajectory correction based on laser radar is performed, including: When the LiDAR magnetic field in the environment is stable, there is only one stable LiDAR magnetic field vector, and the three-dimensional magnetic vector of the magnetometer changes with the attitude of the IMU; when the attitude change of the IMU does not match the change of the magnetic vector, the LiDAR magnetic field environment is unstable under the influence of strong magnets; the attitude change of the IMU is calculated using a gyroscope and expressed in the form of a quaternion. i ; Use quaternion to convert magnetic vector m i Convert to Calculate m i+1 and The modulus of the vector difference is taken as the matching degree su; the larger the matching degree, the greater the attitude difference between the IMU and the magnetic vector, and the unstable magnetic field of the lidar; m i+1 is the i+1th magnetic vector, is the i+1th magnetic vector after transformation; The laser radar magnetic field strength reflects the stability of the laser radar environment. The sliding standard deviation std of the laser radar magnetic field strength is used as the condition for determining the stability of the laser radar magnetic field. The size of the sliding window is set to 100 frames, and the stability of the laser radar magnetic field Defined as: In the formula, Seek guidance for Renault, is the stability value of the lidar magnetic field in the window size s, is the sliding average, thrshd1 is the first window of sliding window size d, and thrshd2 is the second window of sliding window size d; When the average sliding standard deviation and the average matching degree are less than the threshold, the lidar magnetic field is completely stable, and the stability of the lidar magnetic field Set to 1, the stability of the lidar magnetic field The range is from 0 to 1, indicating from unstable to stable; Select the stable LiDAR magnetic field environment and low geological disaster information, and connect the correlation quantity on the variable straight sequence as the anchor point g k If the posture of the geological disaster monitoring equipment sensor is not changed, the correlation value of each straight sequence will fluctuate in a small range; when the fluctuation is greater than the threshold, it is determined that the relative horizontal posture relationship between the geological disaster monitoring equipment sensor and the person has changed, and it is necessary to update g k ; Using g k Calculate the correction angle for a straight sequence The expression is: In the formula, ft i is the average stability of the lidar magnetic field, g i is the i-th anchor point; By using the shortest distance calculation method of the neural network, the correction angle of the straight sequence is predicted Realize adaptive correction of trajectory.

8. The method for automatically discovering geological disaster monitoring equipment based on laser radar according to claim 1 is characterized in that: In step S4, a laser radar digital-analog positioning recognition model based on Kalman filtering is constructed, including: S4.1, build the system model and observation model, and use the data-driven shortest distance calculation method to adopt the local coordinate system A system (x A ,y A ,α A ), select the Northeast Sky coordinate system as the absolute coordinate system B (e B ,n B ,α B ), where α is the displacement direction and the system state vector to be estimated is X p Described as: X p =[e p n p i p ] T In the formula, (e p ,n p ) are the east and north coordinates of the target in the B system at time P, θ p It is the right-handed displacement angle of system A relative to system B, including the initial deflection angle of the two coordinate systems at the moment of positioning service startup and the accumulated error deflection angle of the sensor. Driven by the data of the shortest distance calculation method, the state transition of the system is as follows: Where h( ) is the state vector function to be estimated, O p is the initial state value at time p, e p-1 is the east direction coordinate at time p-1, n p-1 is the north direction coordinate at time p-1, θ p-1 is the right-handed displacement angle of system A relative to system B at time p-1, X p-1 is the system state vector to be estimated at time p-1, Δx p ,Δy p They are the displacement increments output in the horizontal and vertical directions of the shortest distance calculation method in the local coordinate system from time p-1 to time p; The laser radar spectrum positioning estimation adopts the time division multiple access and frequency division multiple access strategies to form a laser radar spectrum signal network; the observation data including at least two laser radar spectra are solved, and the observation equation is obtained through geometric relations, which is expressed as follows: In the formula, C p is the observed data at time p, q( ) is the time division multiple access estimation function, E p is the frequency division multiple access estimated value, F1 is the first time division address, F2 is the second time division address, F i-1 is the i-1th time division address, F i is the i-th time division address, F m-1 is the time division address of the lidar spectrum estimated at the m-1th epoch, F m is the time division address of the lidar spectrum estimated in the mth epoch, i is the number, g is the speed of sound propagation at temperature T, and m is the number of lidar spectra estimated in this epoch; The model is initialized and model parameters are dynamically adjusted.

9. The method for automatically discovering geological disaster monitoring equipment based on laser radar according to claim 8 is characterized in that: Model initialization, including: The observation information is combined with the simple particle filter of the laser radar to calculate the initial value of the plane coordinates of the geological disaster monitoring equipment sensor; the coordinate data contained in the observation information gives the regional range of the geological disaster monitoring equipment sensor, and a certain number of particles are evenly distributed within the range. Through the resampling operation, the particle attributes [e B n B ] T , then the particle set is initialized as: In the formula, e min ,e max ,n min ,n max is the region boundary defined by the initial observation information, N is the number of particles, e l is the property of the lth particle pointing eastward, n l is the property of the lth particle in the north, L( ) is the particle property function of the boundary constraint condition of the region defined by the initial observation information; The overall error of the LiDAR spectrum estimation follows a normal distribution. According to the prediction residual δ, the weight of each particle is: σ=C0-C l In the formula, w l is the weight of the lth particle, R l is the radius of the particle distribution, exp{} is the residual prediction function, σ l is the prediction residual of the lth particle, C0,C l are the initial observation and the predicted observation respectively; After normalizing the particle set weights and using the random resampling method to assign them to a new particle set, the initial value estimate is obtained by averaging: Where, e0 is the original particle property of the east-downward direction, n0 is the original particle property of the north-downward direction, and e l is the property of the lth particle pointing eastward, n l It is the property of the lth particle in the north downward direction; Dynamically adjust model parameters, including: Given the initial system error covariance matrix W0 = I 3×3 Under the premise of , the displacement direction angle converges rapidly, the basic weight of the observation information is improved in the early stage of filtering, and the process noise is amplified, and the observation noise corresponding to each lidar observation data is adjusted. The expression is: In the formula, is the i-th diagonal element of the observation noise covariance matrix, is the initial diagonal element of the observation noise, is the prediction residual of the p+1th lidar spectrum, is the prediction residual of the pth lidar spectrum, P0 is the variance expansion threshold; The geological disaster information connection variability factor of the data-driven shortest distance calculation method model can provide timely guidance on adjusting the status and observation weight when abnormal posture updates caused by sudden changes in the lidar are detected.

10. A laser radar-based automatic detection and recognition system for geological disaster monitoring equipment, characterized in that: The method for automatically discovering geological disaster monitoring equipment based on laser radar according to any one of claims 1 to 9 is implemented, and the system comprises: A module (1) for determining the variability of the connection between the position and geological hazard information is used to construct and train a data-driven geological hazard monitoring equipment movement trajectory evolution model, and to determine the relative position and the variability of the connection between the geological hazard information based on the trained model and the data measured by the geological hazard monitoring equipment sensor; The credibility index prediction module (2) is used to add the error parameter to the data-driven geological disaster monitoring equipment movement trajectory evolution model for training, predict the movement change of geological disaster information in adjacent connection areas and its variance, calculate the connection variability of the geological disaster information of the prediction result, and obtain the prediction credibility index; A trajectory correction module (3), used for performing trajectory correction based on laser radar; The geological disaster monitoring information identification module (4) is used to construct a laser radar digital-analog positioning and identification model based on Kalman filtering, and obtain the automatic geological disaster monitoring equipment positioning and identification results based on the high-frequency displacement rate vector output by the extended Kalman filtering and the geological disaster monitoring equipment moving trajectory evolution model driven by the data, and obtain the geological disaster monitoring information of the location according to the positioning and identification results.

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